Algorithm based on the short-term Rényi entropy and IF estimation for noisy EEG signals analysis.

Comput Biol Med

University of Rijeka, Faculty of Medicine, Ulica Brace Branchetta 20/1, HR-51000 Rijeka, Croatia. Electronic address:

Published: January 2017

AI Article Synopsis

  • Stochastic EEG signals are nonstationary and often consist of multiple components, which can aid in diagnosing brain dysfunctions related to motor control disorders.
  • A new algorithm, based on a modified Rényi entropy technique called short-term Rényi entropy (STRE), has been developed for detecting EEG signal components through time-frequency distribution, enhancing the ability to analyze these signals.
  • This method effectively analyzes limb movement EEG signals in both clean and noisy environments, providing essential spectral information that can improve diagnosis and treatment strategies for neurological disorders.

Article Abstract

Stochastic electroencephalogram (EEG) signals are known to be nonstationary and often multicomponential. Detecting and extracting their components may help clinicians to localize brain neurological dysfunctionalities for patients with motor control disorders due to the fact that movement-related cortical activities are reflected in spectral EEG changes. A new algorithm for EEG signal components detection from its time-frequency distribution (TFD) has been proposed in this paper. The algorithm utilizes the modification of the Rényi entropy-based technique for number of components estimation, called short-term Rényi entropy (STRE), and upgraded by an iterative algorithm which was shown to enhance existing approaches. Combined with instantaneous frequency (IF) estimation, the proposed method was applied to EEG signal analysis both in noise-free and noisy environments for limb movements EEG signals, and was shown to be an efficient technique providing spectral description of brain activities at each electrode location up to moderate additive noise levels. Furthermore, the obtained information concerning the number of EEG signal components and their IFs show potentials to enhance diagnostics and treatment of neurological disorders for patients with motor control illnesses.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.compbiomed.2016.11.002DOI Listing

Publication Analysis

Top Keywords

eeg signals
12
eeg signal
12
short-term rényi
8
rényi entropy
8
patients motor
8
motor control
8
signal components
8
eeg
7
algorithm
4
algorithm based
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!